{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp models.itransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "import logging\n",
    "import warnings\n",
    "from fastcore.test import test_eq\n",
    "from nbdev.showdoc import show_doc\n",
    "from neuralforecast.common._model_checks import check_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The iTransformer model simply takes the Transformer architecture but it applies the attention and feed-forward network on the inverted dimensions. This means that time points of each individual series are embedded into tokens. That way, the attention mechanisms learn multivariate correlation and the feed-forward network learns non-linear relationships.\n",
    "\n",
    "**References**\n",
    "- [Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long. \"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting\"](https://arxiv.org/abs/2310.06625)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Figure 1. Architecture of iTransformer.](imgs_models/itransformer.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from typing import Optional\n",
    "from neuralforecast.losses.pytorch import MAE\n",
    "from neuralforecast.common._base_model import BaseModel\n",
    "\n",
    "from neuralforecast.common._modules import (\n",
    "    TransEncoder, \n",
    "    TransEncoderLayer, \n",
    "    AttentionLayer, \n",
    "    FullAttention, \n",
    "    DataEmbedding_inverted\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "\n",
    "class iTransformer(BaseModel):\n",
    "\n",
    "    \"\"\" iTransformer\n",
    "\n",
    "    **Parameters:**<br>\n",
    "    `h`: int, Forecast horizon. <br>\n",
    "    `input_size`: int, autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].<br>\n",
    "    `n_series`: int, number of time-series.<br>\n",
    "    `futr_exog_list`: str list, future exogenous columns.<br>\n",
    "    `hist_exog_list`: str list, historic exogenous columns.<br>\n",
    "    `stat_exog_list`: str list, static exogenous columns.<br>\n",
    "    `exclude_insample_y`: bool=False, the model skips the autoregressive features y[t-input_size:t] if True.<br>    \n",
    "    `hidden_size`: int, dimension of the model.<br>\n",
    "    `n_heads`: int, number of heads.<br>\n",
    "    `e_layers`: int, number of encoder layers.<br>\n",
    "    `d_layers`: int, number of decoder layers.<br>\n",
    "    `d_ff`: int, dimension of fully-connected layer.<br>\n",
    "    `factor`: int, attention factor.<br>\n",
    "    `dropout`: float, dropout rate.<br>\n",
    "    `use_norm`: bool, whether to normalize or not.<br>\n",
    "    `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>\n",
    "    `valid_loss`: PyTorch module=`loss`, instantiated valid loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>\n",
    "    `max_steps`: int=1000, maximum number of training steps.<br>\n",
    "    `learning_rate`: float=1e-3, Learning rate between (0, 1).<br>\n",
    "    `num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>\n",
    "    `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>\n",
    "    `val_check_steps`: int=100, Number of training steps between every validation loss check.<br>\n",
    "    `batch_size`: int=32, number of different series in each batch.<br>\n",
    "    `valid_batch_size`: int=None, number of different series in each validation and test batch, if None uses batch_size.<br>\n",
    "    `windows_batch_size`: int=32, number of windows to sample in each training batch, default uses all.<br>\n",
    "    `inference_windows_batch_size`: int=32, number of windows to sample in each inference batch, -1 uses all.<br>\n",
    "    `start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.<br>\n",
    "    `step_size`: int=1, step size between each window of temporal data.<br>\n",
    "    `scaler_type`: str='identity', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>\n",
    "    `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>\n",
    "    `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n",
    "    `alias`: str, optional,  Custom name of the model.<br>\n",
    "    `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n",
    "    `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n",
    "    `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n",
    "    `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n",
    "    `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n",
    "    `**trainer_kwargs`: int,  keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n",
    "    \n",
    "    **References**<br>\n",
    "    - [Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long. \"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting\"](https://arxiv.org/abs/2310.06625)\n",
    "    \"\"\"\n",
    "\n",
    "    # Class attributes\n",
    "    EXOGENOUS_FUTR = False\n",
    "    EXOGENOUS_HIST = False\n",
    "    EXOGENOUS_STAT = False\n",
    "    MULTIVARIATE = True\n",
    "    RECURRENT = False\n",
    "\n",
    "    def __init__(self,\n",
    "                 h,\n",
    "                 input_size,\n",
    "                 n_series,\n",
    "                 futr_exog_list = None,\n",
    "                 hist_exog_list = None,\n",
    "                 stat_exog_list = None,\n",
    "                 exclude_insample_y = False,\n",
    "                 hidden_size: int = 512,\n",
    "                 n_heads: int = 8,\n",
    "                 e_layers: int = 2,\n",
    "                 d_layers: int = 1,\n",
    "                 d_ff: int = 2048,\n",
    "                 factor: int = 1,\n",
    "                 dropout: float = 0.1,\n",
    "                 use_norm: bool = True,\n",
    "                 loss = MAE(),\n",
    "                 valid_loss = None,\n",
    "                 max_steps: int = 1000,\n",
    "                 learning_rate: float = 1e-3,\n",
    "                 num_lr_decays: int = -1,\n",
    "                 early_stop_patience_steps: int =-1,\n",
    "                 val_check_steps: int = 100,\n",
    "                 batch_size: int = 32,\n",
    "                 valid_batch_size: Optional[int] = None,\n",
    "                 windows_batch_size = 32,\n",
    "                 inference_windows_batch_size = 32,\n",
    "                 start_padding_enabled = False,\n",
    "                 step_size: int = 1,\n",
    "                 scaler_type: str = 'identity',\n",
    "                 random_seed: int = 1,\n",
    "                 drop_last_loader: bool = False,\n",
    "                 alias: Optional[str] = None,\n",
    "                 optimizer = None,\n",
    "                 optimizer_kwargs = None,\n",
    "                 lr_scheduler = None,\n",
    "                 lr_scheduler_kwargs = None,  \n",
    "                 dataloader_kwargs = None,          \n",
    "                 **trainer_kwargs):\n",
    "        \n",
    "        super(iTransformer, self).__init__(h=h,\n",
    "                                           input_size=input_size,\n",
    "                                           n_series=n_series,\n",
    "                                           futr_exog_list = futr_exog_list,\n",
    "                                           hist_exog_list = hist_exog_list,\n",
    "                                           stat_exog_list = stat_exog_list,\n",
    "                                           exclude_insample_y = exclude_insample_y,\n",
    "                                           loss=loss,\n",
    "                                           valid_loss=valid_loss,\n",
    "                                           max_steps=max_steps,\n",
    "                                           learning_rate=learning_rate,\n",
    "                                           num_lr_decays=num_lr_decays,\n",
    "                                           early_stop_patience_steps=early_stop_patience_steps,\n",
    "                                           val_check_steps=val_check_steps,\n",
    "                                           batch_size=batch_size,\n",
    "                                           valid_batch_size=valid_batch_size,\n",
    "                                           windows_batch_size=windows_batch_size,\n",
    "                                           inference_windows_batch_size=inference_windows_batch_size,\n",
    "                                           start_padding_enabled=start_padding_enabled,\n",
    "                                           step_size=step_size,\n",
    "                                           scaler_type=scaler_type,\n",
    "                                           random_seed=random_seed,\n",
    "                                           drop_last_loader=drop_last_loader,\n",
    "                                           alias=alias,\n",
    "                                           optimizer=optimizer,\n",
    "                                           optimizer_kwargs=optimizer_kwargs,\n",
    "                                           lr_scheduler=lr_scheduler,\n",
    "                                           lr_scheduler_kwargs=lr_scheduler_kwargs,\n",
    "                                           dataloader_kwargs=dataloader_kwargs,\n",
    "                                           **trainer_kwargs)\n",
    "               \n",
    "        self.enc_in = n_series\n",
    "        self.dec_in = n_series\n",
    "        self.c_out = n_series\n",
    "        self.hidden_size = hidden_size\n",
    "        self.n_heads = n_heads\n",
    "        self.e_layers = e_layers\n",
    "        self.d_layers = d_layers\n",
    "        self.d_ff = d_ff\n",
    "        self.factor = factor\n",
    "        self.dropout = dropout\n",
    "        self.use_norm = use_norm\n",
    "\n",
    "        # Architecture\n",
    "        self.enc_embedding = DataEmbedding_inverted(input_size, self.hidden_size, self.dropout)\n",
    "\n",
    "        self.encoder = TransEncoder(\n",
    "            [\n",
    "                TransEncoderLayer(\n",
    "                    AttentionLayer(\n",
    "                        FullAttention(False, self.factor, attention_dropout=self.dropout), self.hidden_size, self.n_heads),\n",
    "                    self.hidden_size,\n",
    "                    self.d_ff,\n",
    "                    dropout=self.dropout,\n",
    "                    activation=F.gelu\n",
    "                ) for l in range(self.e_layers)\n",
    "            ],\n",
    "            norm_layer=torch.nn.LayerNorm(self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.projector = nn.Linear(self.hidden_size, h * self.loss.outputsize_multiplier, bias=True)\n",
    "\n",
    "    def forecast(self, x_enc):\n",
    "        if self.use_norm:\n",
    "            # Normalization from Non-stationary Transformer\n",
    "            means = x_enc.mean(1, keepdim=True).detach()\n",
    "            x_enc = x_enc - means\n",
    "            stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)\n",
    "            x_enc /= stdev\n",
    "\n",
    "        _, _, N = x_enc.shape # B L N\n",
    "        # B: batch_size;       E: hidden_size; \n",
    "        # L: input_size;       S: horizon(h);\n",
    "        # N: number of variate (tokens), can also includes covariates\n",
    "\n",
    "        # Embedding\n",
    "        # B L N -> B N E                (B L N -> B L E in the vanilla Transformer)\n",
    "        enc_out = self.enc_embedding(x_enc, None) # covariates (e.g timestamp) can be also embedded as tokens\n",
    "        \n",
    "        # B N E -> B N E                (B L E -> B L E in the vanilla Transformer)\n",
    "        # the dimensions of embedded time series has been inverted, and then processed by native attn, layernorm and ffn modules\n",
    "        enc_out, attns = self.encoder(enc_out, attn_mask=None)\n",
    "\n",
    "        # B N E -> B N S -> B S N \n",
    "        dec_out = self.projector(enc_out).permute(0, 2, 1)[:, :, :N] # filter the covariates\n",
    "\n",
    "        if self.use_norm:\n",
    "            # De-Normalization from Non-stationary Transformer\n",
    "            dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.h * self.loss.outputsize_multiplier, 1))\n",
    "            dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.h * self.loss.outputsize_multiplier, 1))\n",
    "\n",
    "        return dec_out\n",
    "    \n",
    "    def forward(self, windows_batch):\n",
    "        insample_y = windows_batch['insample_y']\n",
    "\n",
    "        y_pred = self.forecast(insample_y)\n",
    "        y_pred = y_pred.reshape(insample_y.shape[0],\n",
    "                                self.h,\n",
    "                                -1)\n",
    "\n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_doc(iTransformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_doc(iTransformer.fit, name='iTransformer.fit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_doc(iTransformer.predict, name='iTransformer.predict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "# Unit tests for models\n",
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)\n",
    "logging.getLogger(\"lightning_fabric\").setLevel(logging.ERROR)\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    check_model(iTransformer, [\"airpassengers\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Usage example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| eval: false\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import iTransformer\n",
    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic\n",
    "from neuralforecast.losses.pytorch import MSE\n",
    "\n",
    "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 132 train\n",
    "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
    "\n",
    "model = iTransformer(h=12,\n",
    "                     input_size=24,\n",
    "                     n_series=2,\n",
    "                     hidden_size=128,\n",
    "                     n_heads=2,\n",
    "                     e_layers=2,\n",
    "                     d_layers=1,\n",
    "                     d_ff=4,\n",
    "                     factor=1,\n",
    "                     dropout=0.1,\n",
    "                     use_norm=True,\n",
    "                     loss=MSE(),\n",
    "                     valid_loss=MAE(),\n",
    "                     early_stop_patience_steps=3,\n",
    "                     batch_size=32,\n",
    "                     max_steps=100)\n",
    "\n",
    "fcst = NeuralForecast(models=[model], freq='ME')\n",
    "fcst.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)\n",
    "forecasts = fcst.predict(futr_df=Y_test_df)\n",
    "\n",
    "# Plot predictions\n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
    "Y_hat_df = forecasts.reset_index(drop=False).drop(columns=['unique_id','ds'])\n",
    "plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)\n",
    "plot_df = pd.concat([Y_train_df, plot_df])\n",
    "\n",
    "plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)\n",
    "plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
    "plt.plot(plot_df['ds'], plot_df['iTransformer'], c='blue', label='Forecast')\n",
    "ax.set_title('AirPassengers Forecast', fontsize=22)\n",
    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
    "ax.set_xlabel('Year', fontsize=20)\n",
    "ax.legend(prop={'size': 15})\n",
    "ax.grid()"
   ]
  }
 ],
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